b'Amy Zhang'

research

∙ 04/27/2022

Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning

Building generalizable goal-conditioned agents from rich observations is...

Philippe Hansen-Estruch, et al. ∙

research

∙ 02/17/2022

Designing Word Filter Tools for Creator-led Comment Moderation

Online social platforms centered around content creators often allow com...

Shagun Jhaver, et al. ∙

research

∙ 02/14/2022

Robust Policy Learning over Multiple Uncertainty Sets

Reinforcement learning (RL) agents need to be robust to variations in sa...

Annie Xie, et al. ∙

research

∙ 02/11/2022

Online Decision Transformer

Recent work has shown that offline reinforcement learning (RL) can be fo...

Qinqing Zheng, et al. ∙

research

∙ 01/09/2022

Information Borrowing in Regression Models

Model development often takes data structure, subject matter considerati...

Amy Zhang, et al. ∙

research

∙ 11/18/2021

A Survey of Generalisation in Deep Reinforcement Learning

The study of generalisation in deep Reinforcement Learning (RL) aims to ...

Robert Kirk, et al. ∙

research

∙ 11/15/2021

Learning Representations for Pixel-based Control: What Matters and Why?

Learning representations for pixel-based control has garnered significan...

Manan Tomar, et al. ∙

research

∙ 10/13/2021

Block Contextual MDPs for Continual Learning

In reinforcement learning (RL), when defining a Markov Decision Process ...

Shagun Sodhani, et al. ∙

research

∙ 08/27/2021

Designing for Multiple Centers of Power: A Taxonomy of Multi-level Governance in Online Social Platforms

Many have criticized the centralized and unaccountable governance of pro...

Shagun Jhaver, et al. ∙

research

∙ 07/13/2021

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability

Generalization is a central challenge for the deployment of reinforcemen...

Dibya Ghosh, et al. ∙

research

∙ 06/22/2021

Provably Efficient Representation Learning in Low-rank Markov Decision Processes

The success of deep reinforcement learning (DRL) is due to the power of ...

Weitong Zhang, et al. ∙

research

∙ 04/20/2021

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

Model-based reinforcement learning is a compelling framework for data-ef...

Luis Pineda, et al. ∙

research

∙ 02/19/2021

Model-Invariant State Abstractions for Model-Based Reinforcement Learning

Accuracy and generalization of dynamics models is key to the success of ...

Manan Tomar, et al. ∙

research

∙ 02/11/2021

Multi-Task Reinforcement Learning with Context-based Representations

The benefit of multi-task learning over single-task learning relies on t...

Shagun Sodhani, et al. ∙

research

∙ 01/27/2021

Pano: Engaging with News using Moral Framing towards Bridging Ideological Divides

Society is showing signs of strong ideological polarization. When pushed...

Jessica Wang, et al. ∙

research

∙ 12/09/2020

Automating Document Classification with Distant Supervision to Increase the Efficiency of Systematic Reviews

Objective: Systematic reviews of scholarly documents often provide compl...

Xiaoxiao Li, et al. ∙

research

∙ 12/03/2020

Intervention Design for Effective Sim2Real Transfer

The goal of this work is to address the recent success of domain randomi...

Melissa Mozifian, et al. ∙

research

∙ 07/14/2020

Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP

Multi-task reinforcement learning is a rich paradigm where information f...

Amy Zhang, et al. ∙

research

∙ 06/18/2020

Learning Invariant Representations for Reinforcement Learning without Reconstruction

We study how representation learning can accelerate reinforcement learni...

Amy Zhang, et al. ∙

research

∙ 05/07/2020

Plan2Vec: Unsupervised Representation Learning by Latent Plans

In this paper we introduce plan2vec, an unsupervised representation lear...

Ge Yang, et al. ∙

research

∙ 03/12/2020

Invariant Causal Prediction for Block MDPs

Generalization across environments is critical to the successful applica...

Amy Zhang, et al. ∙

research

∙ 03/09/2020

Stable Policy Optimization via Off-Policy Divergence Regularization

Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization...

Ahmed Touati, et al. ∙

research

∙ 03/02/2020

Out-of-Distribution Generalization via Risk Extrapolation (REx)

Generalizing outside of the training distribution is an open challenge f...

David Krueger, et al. ∙

research

∙ 10/02/2019

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Training an agent to solve control tasks directly from high-dimensional ...

Denis Yarats, et al. ∙

research

∙ 06/25/2019

Learning Causal State Representations of Partially Observable Environments

Intelligent agents can cope with sensory-rich environments by learning t...

Amy Zhang, et al. ∙

research

∙ 11/14/2018

Natural Environment Benchmarks for Reinforcement Learning

While current benchmark reinforcement learning (RL) tasks have been usef...

Amy Zhang, et al. ∙

research

∙ 06/20/2018

A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning

The risks and perils of overfitting in machine learning are well known. ...

Amy Zhang, et al. ∙

research

∙ 04/27/2018

Decoupling Dynamics and Reward for Transfer Learning

Current reinforcement learning (RL) methods can successfully learn singl...

Amy Zhang, et al. ∙

research

∙ 03/01/2018

Composable Planning with Attributes

The tasks that an agent will need to solve often are not known during tr...

Amy Zhang, et al. ∙

research

∙ 12/15/2017

Mapping the world population one building at a time

High resolution datasets of population density which accurately map spar...

Tobias G. Tiecke, et al. ∙

research

∙ 07/27/2017

Building Detection from Satellite Images on a Global Scale

In the last several years, remote sensing technology has opened up the p...

Amy Zhang, et al. ∙

research

∙ 12/08/2016

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation

Learning from weakly-supervised data is one of the main challenges in ma...

Xianming Liu, et al. ∙

research

∙ 08/09/2014

Guess Who Rated This Movie: Identifying Users Through Subspace Clustering

It is often the case that, within an online recommender system, multiple...

Amy Zhang, et al. ∙

Amy Zhang

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